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Ibn Al-Haytham

hazen

Quality assurance framework for Magnetic Resonance Imaging
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Overview

hazen is a command line tool (Python package) for performing automated analysis of magnetic resonance imaging (MRI) quality assurance (QA) data. hazen consists of multiple Tasks which perform quantitative processing and analysis of MRI phantom data. Currently, hazen supports the ACR Large MRI Phantom and the MagNET Test Objects collection of phantoms.

The hazen Tasks provide the following measurements within these phantoms:

  • Signal-to-noise ratio (SNR)
  • Spatial resolution
  • Slice position
  • Slice width
  • Uniformity
  • Ghosting
  • MR relaxometry

Each Task outputs numerical results to the user's terminal. Below is an output from the hazen snr Task performed on some example MRI data:

hazen snr tests/data/snr/Siemens
{
  'snr_smoothing_measured_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 173.97,
  'snr_smoothing_measured_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_3_1': 177.91,
  'snr_smoothing_normalised_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 1698.21,
  'snr_smoothing_normalised_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_3_1': 1736.66,
  'snr_subtraction_measured_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 220.73,
  'snr_subtraction_normalised_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 2154.69
}

The optional --report flag allows the user to generate diagrams that visualise the image processing performed by each hazen Task:

hazen snr tests/data/snr/Siemens --report hazen acr_ghosting tests/data/acr/Siemens --report

Installation and usage

There are two main options for running hazen.

  1. Install using Python and run directly via command line interface (CLI)
  2. Run using the latest Docker container build

1) Python install and run (CLI)

hazen can be installed with Python 3.9, 3.10 or 3.11 versions via pip (Python 3.12 is not currently supported).

It is strongly recommended to use a virtual environment, that can be made using the following commands.

python3 -m venv hazen-venv
source hazen-venv/bin/activate
pip install hazen
# Check that hazen was correctly installed and see which version it is:
hazen --version

Updating hazen

If you already have an old version of hazen installed, upgrade to the latest version with:

source hazen-venv/bin/activate
pip install --upgrade pip
pip install --upgrade hazen

Running hazen via CLI

The CLI version of hazen is designed to be pointed at single folders containing DICOM file(s). Example datasets are provided in the tests/data/ directory. If you are using the Docker version of hazen (installation described below), replace hazen with hazen-app in the following commands.

# To see the full list of available Tasks and optional arguments, enter:
hazen -h

# To perform the SNR Task on example data:
hazen snr tests/data/snr/Philips

# The `--report` option generates additional visualisation about the image processing measurement methods and is available 
# for all Tasks. Example usage for the SNR Task, which returns images showing the regions used for SNR calculation.
hazen snr tests/data/snr/Philips --report

# a directory path can be provided to save the report images to:
hazen snr tests/data/snr/Philips --report ./report_images

2) Docker

The Docker version of hazen has been made available as it is easy to get up-and-running and is linked to the most recent stable release. Refer to the Docker installation instructions to install Docker on your host computer.

The containerised version of hazen can be obtained from DockerHub (see commands below). For ease of use, it is recommended to copy the hazen-app script to a location accessible on the PATH such as /usr/local/bin. This will allow you to run hazen from any directory on your computer. Then, to use Docker hazen, simply run the hazen-app script appended with the function you want to use (e.g.: snr).

In Terminal:

# Ensure Docker installed and running, then pull the latest hazen Docker container
docker pull gsttmriphysics/hazen:latest

# Command line output will look something like:
latest: Pulling from gsttmriphysics/hazen
Digest: sha256:18603e40b45f3af4bf45f07559a08a7833af92a6efe21cb7306f758e8eeab24a
Status: Image is up to date for gsttmriphysics/hazen:latest
docker.io/gsttmriphysics/hazen:latest

# Copy the 'hazen-app' executable file into your local bin folder
cd hazen
cp hazen-app /usr/local/bin

# Run hazen via Docker with the normal CLI inputs
hazen-app snr tests/data/snr/Siemens/

# Example command line output for the SNR Task:
{
  'snr_smoothing_measured_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 173.97,
  'snr_smoothing_measured_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_3_1': 177.91,
  'snr_smoothing_normalised_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 1698.21,
  'snr_smoothing_normalised_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_3_1': 1736.66,
  'snr_subtraction_measured_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 220.73,
  'snr_subtraction_normalised_SNR_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 2154.69
}

Web Interface

Development of a web interface for hazen is in progress.

Contributing to hazen

Users

Please raise an Issue for any of the following reasons:

  • Problems installing or running hazen
  • Suggestions for improvements
  • Requests for new features

We have used hazen with MRI data from a handful of different MRI scanners, including multiple different vendors. If hazen does not perform with your MRI data, or the results are unexpected, please raise an Issue.

Developers

Please see CONTRIBUTING.md for developer guidelines.